Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations3656
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory485.6 KiB
Average record size in memory136.0 B

Variable types

Categorical8
Numeric8

Alerts

cigsPerDay is highly overall correlated with currentSmokerHigh correlation
currentSmoker is highly overall correlated with cigsPerDayHigh correlation
diaBP is highly overall correlated with prevalentHyp and 1 other fieldsHigh correlation
diabetes is highly overall correlated with glucoseHigh correlation
glucose is highly overall correlated with diabetesHigh correlation
prevalentHyp is highly overall correlated with diaBP and 1 other fieldsHigh correlation
sysBP is highly overall correlated with diaBP and 1 other fieldsHigh correlation
BPMeds is highly imbalanced (80.4%)Imbalance
prevalentStroke is highly imbalanced (94.9%)Imbalance
diabetes is highly imbalanced (82.0%)Imbalance
cigsPerDay has 1868 (51.1%) zerosZeros

Reproduction

Analysis started2024-11-11 05:06:25.929401
Analysis finished2024-11-11 05:06:28.570885
Duration2.64 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

male
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
0
2034 
1
1622 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2034
55.6%
1 1622
44.4%

Length

2024-11-10T23:06:28.608021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-10T23:06:28.649138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2034
55.6%
1 1622
44.4%

Most occurring characters

ValueCountFrequency (%)
0 2034
55.6%
1 1622
44.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2034
55.6%
1 1622
44.4%

Most occurring scripts

ValueCountFrequency (%)
Common 3656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2034
55.6%
1 1622
44.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2034
55.6%
1 1622
44.4%

age
Real number (ℝ)

Distinct39
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.55744
Minimum32
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2024-11-10T23:06:28.690894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile37
Q142
median49
Q356
95-th percentile64
Maximum70
Range38
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.5611335
Coefficient of variation (CV)0.17275173
Kurtosis-0.99161978
Mean49.55744
Median Absolute Deviation (MAD)7
Skewness0.2311704
Sum181182
Variance73.293006
MonotonicityNot monotonic
2024-11-10T23:06:28.737893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
40 166
 
4.5%
46 166
 
4.5%
42 161
 
4.4%
48 149
 
4.1%
39 146
 
4.0%
41 145
 
4.0%
44 143
 
3.9%
45 140
 
3.8%
43 137
 
3.7%
52 129
 
3.5%
Other values (29) 2174
59.5%
ValueCountFrequency (%)
32 1
 
< 0.1%
33 5
 
0.1%
34 14
 
0.4%
35 33
 
0.9%
36 77
2.1%
37 80
2.2%
38 124
3.4%
39 146
4.0%
40 166
4.5%
41 145
4.0%
ValueCountFrequency (%)
70 1
 
< 0.1%
69 5
 
0.1%
68 16
 
0.4%
67 38
 
1.0%
66 34
 
0.9%
65 46
1.3%
64 80
2.2%
63 96
2.6%
62 91
2.5%
61 91
2.5%

education
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
1.0
1526 
2.0
1101 
3.0
606 
4.0
423 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10968
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 1526
41.7%
2.0 1101
30.1%
3.0 606
 
16.6%
4.0 423
 
11.6%

Length

2024-11-10T23:06:28.914226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-10T23:06:28.953524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1526
41.7%
2.0 1101
30.1%
3.0 606
 
16.6%
4.0 423
 
11.6%

Most occurring characters

ValueCountFrequency (%)
. 3656
33.3%
0 3656
33.3%
1 1526
13.9%
2 1101
 
10.0%
3 606
 
5.5%
4 423
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7312
66.7%
Other Punctuation 3656
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3656
50.0%
1 1526
20.9%
2 1101
 
15.1%
3 606
 
8.3%
4 423
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 3656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10968
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3656
33.3%
0 3656
33.3%
1 1526
13.9%
2 1101
 
10.0%
3 606
 
5.5%
4 423
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3656
33.3%
0 3656
33.3%
1 1526
13.9%
2 1101
 
10.0%
3 606
 
5.5%
4 423
 
3.9%

currentSmoker
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
0
1868 
1
1788 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1868
51.1%
1 1788
48.9%

Length

2024-11-10T23:06:28.994662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-10T23:06:29.030253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1868
51.1%
1 1788
48.9%

Most occurring characters

ValueCountFrequency (%)
0 1868
51.1%
1 1788
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1868
51.1%
1 1788
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common 3656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1868
51.1%
1 1788
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1868
51.1%
1 1788
48.9%

cigsPerDay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0221554
Minimum0
Maximum70
Zeros1868
Zeros (%)51.1%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2024-11-10T23:06:29.067514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile30
Maximum70
Range70
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.918869
Coefficient of variation (CV)1.3210666
Kurtosis0.9618248
Mean9.0221554
Median Absolute Deviation (MAD)0
Skewness1.2298317
Sum32985
Variance142.05943
MonotonicityNot monotonic
2024-11-10T23:06:29.111423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 1868
51.1%
20 651
 
17.8%
30 191
 
5.2%
15 184
 
5.0%
10 123
 
3.4%
5 99
 
2.7%
9 99
 
2.7%
3 83
 
2.3%
40 69
 
1.9%
1 61
 
1.7%
Other values (23) 228
 
6.2%
ValueCountFrequency (%)
0 1868
51.1%
1 61
 
1.7%
2 16
 
0.4%
3 83
 
2.3%
4 8
 
0.2%
5 99
 
2.7%
6 17
 
0.5%
7 11
 
0.3%
8 9
 
0.2%
9 99
 
2.7%
ValueCountFrequency (%)
70 1
 
< 0.1%
60 9
 
0.2%
50 4
 
0.1%
45 3
 
0.1%
43 49
 
1.3%
40 69
 
1.9%
38 1
 
< 0.1%
35 19
 
0.5%
30 191
5.2%
29 1
 
< 0.1%

BPMeds
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
0.0
3545 
1.0
 
111

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10968
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3545
97.0%
1.0 111
 
3.0%

Length

2024-11-10T23:06:29.152562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-10T23:06:29.187759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3545
97.0%
1.0 111
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 7201
65.7%
. 3656
33.3%
1 111
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7312
66.7%
Other Punctuation 3656
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7201
98.5%
1 111
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 3656
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10968
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7201
65.7%
. 3656
33.3%
1 111
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7201
65.7%
. 3656
33.3%
1 111
 
1.0%

prevalentStroke
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
0
3635 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3635
99.4%
1 21
 
0.6%

Length

2024-11-10T23:06:29.225992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-10T23:06:29.261572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3635
99.4%
1 21
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 3635
99.4%
1 21
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3635
99.4%
1 21
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3635
99.4%
1 21
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3635
99.4%
1 21
 
0.6%

prevalentHyp
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
0
2517 
1
1139 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2517
68.8%
1 1139
31.2%

Length

2024-11-10T23:06:29.299371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-10T23:06:29.334997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2517
68.8%
1 1139
31.2%

Most occurring characters

ValueCountFrequency (%)
0 2517
68.8%
1 1139
31.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2517
68.8%
1 1139
31.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2517
68.8%
1 1139
31.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2517
68.8%
1 1139
31.2%

diabetes
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
0
3557 
1
 
99

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3557
97.3%
1 99
 
2.7%

Length

2024-11-10T23:06:29.372869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-10T23:06:29.410308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3557
97.3%
1 99
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 3557
97.3%
1 99
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3557
97.3%
1 99
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3557
97.3%
1 99
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3557
97.3%
1 99
 
2.7%

totChol
Real number (ℝ)

Distinct241
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.87309
Minimum113
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2024-11-10T23:06:29.452541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum113
5-th percentile170
Q1206
median234
Q3263.25
95-th percentile312
Maximum600
Range487
Interquartile range (IQR)57.25

Descriptive statistics

Standard deviation44.096223
Coefficient of variation (CV)0.1861597
Kurtosis1.8423574
Mean236.87309
Median Absolute Deviation (MAD)29
Skewness0.6637004
Sum866008
Variance1944.4769
MonotonicityNot monotonic
2024-11-10T23:06:29.501041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240 69
 
1.9%
260 58
 
1.6%
220 58
 
1.6%
232 54
 
1.5%
210 51
 
1.4%
230 50
 
1.4%
250 48
 
1.3%
200 48
 
1.3%
225 46
 
1.3%
205 45
 
1.2%
Other values (231) 3129
85.6%
ValueCountFrequency (%)
113 1
 
< 0.1%
119 1
 
< 0.1%
124 1
 
< 0.1%
133 1
 
< 0.1%
135 2
0.1%
137 1
 
< 0.1%
140 2
0.1%
143 3
0.1%
144 2
0.1%
145 1
 
< 0.1%
ValueCountFrequency (%)
600 1
 
< 0.1%
464 1
 
< 0.1%
453 1
 
< 0.1%
439 1
 
< 0.1%
432 1
 
< 0.1%
410 3
0.1%
405 1
 
< 0.1%
398 1
 
< 0.1%
392 1
 
< 0.1%
391 1
 
< 0.1%

sysBP
Real number (ℝ)

HIGH CORRELATION 

Distinct231
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.36803
Minimum83.5
Maximum295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2024-11-10T23:06:29.545677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum83.5
5-th percentile104
Q1117
median128
Q3144
95-th percentile175
Maximum295
Range211.5
Interquartile range (IQR)27

Descriptive statistics

Standard deviation22.092444
Coefficient of variation (CV)0.16690167
Kurtosis2.2766967
Mean132.36803
Median Absolute Deviation (MAD)13
Skewness1.1636945
Sum483937.5
Variance488.07608
MonotonicityNot monotonic
2024-11-10T23:06:29.590979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130 90
 
2.5%
120 87
 
2.4%
110 83
 
2.3%
125 79
 
2.2%
115 75
 
2.1%
124 73
 
2.0%
122 70
 
1.9%
128 68
 
1.9%
116 65
 
1.8%
132 63
 
1.7%
Other values (221) 2903
79.4%
ValueCountFrequency (%)
83.5 2
 
0.1%
85 1
 
< 0.1%
85.5 1
 
< 0.1%
90 2
 
0.1%
92 1
 
< 0.1%
92.5 2
 
0.1%
93 2
 
0.1%
93.5 1
 
< 0.1%
94 3
0.1%
95 5
0.1%
ValueCountFrequency (%)
295 1
< 0.1%
248 1
< 0.1%
244 1
< 0.1%
243 1
< 0.1%
232 1
< 0.1%
230 1
< 0.1%
220 2
0.1%
217 1
< 0.1%
215 2
0.1%
214 1
< 0.1%

diaBP
Real number (ℝ)

HIGH CORRELATION 

Distinct142
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.912062
Minimum48
Maximum142.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2024-11-10T23:06:29.634617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile66
Q175
median82
Q390
95-th percentile105
Maximum142.5
Range94.5
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.974825
Coefficient of variation (CV)0.14442802
Kurtosis1.2616823
Mean82.912062
Median Absolute Deviation (MAD)7.5
Skewness0.7103882
Sum303126.5
Variance143.39644
MonotonicityNot monotonic
2024-11-10T23:06:29.681489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 217
 
5.9%
82 138
 
3.8%
85 119
 
3.3%
70 114
 
3.1%
81 114
 
3.1%
84 104
 
2.8%
78 104
 
2.8%
90 103
 
2.8%
87 97
 
2.7%
86 94
 
2.6%
Other values (132) 2452
67.1%
ValueCountFrequency (%)
48 1
 
< 0.1%
51 1
 
< 0.1%
52 2
 
0.1%
53 1
 
< 0.1%
54 1
 
< 0.1%
55 3
0.1%
56 2
 
0.1%
57 5
0.1%
57.5 2
 
0.1%
58 4
0.1%
ValueCountFrequency (%)
142.5 1
 
< 0.1%
140 1
 
< 0.1%
136 1
 
< 0.1%
135 2
 
0.1%
133 2
 
0.1%
132 1
 
< 0.1%
130 5
0.1%
128 1
 
< 0.1%
127.5 1
 
< 0.1%
125 3
0.1%

BMI
Real number (ℝ)

Distinct1297
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.784185
Minimum15.54
Maximum56.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2024-11-10T23:06:29.725049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15.54
5-th percentile20.0575
Q123.08
median25.38
Q328.04
95-th percentile32.6925
Maximum56.8
Range41.26
Interquartile range (IQR)4.96

Descriptive statistics

Standard deviation4.0659127
Coefficient of variation (CV)0.15769018
Kurtosis2.8349407
Mean25.784185
Median Absolute Deviation (MAD)2.47
Skewness0.99937349
Sum94266.98
Variance16.531646
MonotonicityNot monotonic
2024-11-10T23:06:29.773551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.48 18
 
0.5%
22.54 16
 
0.4%
22.91 15
 
0.4%
25.09 14
 
0.4%
22.19 14
 
0.4%
23.1 13
 
0.4%
25.23 13
 
0.4%
23.09 13
 
0.4%
22.73 12
 
0.3%
22.9 12
 
0.3%
Other values (1287) 3516
96.2%
ValueCountFrequency (%)
15.54 1
< 0.1%
15.96 1
< 0.1%
16.48 1
< 0.1%
16.59 2
0.1%
16.69 1
< 0.1%
16.71 1
< 0.1%
16.73 1
< 0.1%
16.75 1
< 0.1%
16.87 1
< 0.1%
16.92 1
< 0.1%
ValueCountFrequency (%)
56.8 1
< 0.1%
51.28 1
< 0.1%
45.8 1
< 0.1%
44.71 1
< 0.1%
44.55 1
< 0.1%
44.27 1
< 0.1%
44.09 1
< 0.1%
43.69 1
< 0.1%
43.67 1
< 0.1%
43.48 1
< 0.1%

heartRate
Real number (ℝ)

Distinct72
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.73058
Minimum44
Maximum143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2024-11-10T23:06:29.819011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile60
Q168
median75
Q382
95-th percentile96.25
Maximum143
Range99
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.982952
Coefficient of variation (CV)0.15823135
Kurtosis1.0625405
Mean75.73058
Median Absolute Deviation (MAD)7
Skewness0.67098224
Sum276871
Variance143.59114
MonotonicityNot monotonic
2024-11-10T23:06:29.867124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 507
 
13.9%
80 336
 
9.2%
70 269
 
7.4%
60 207
 
5.7%
85 191
 
5.2%
72 184
 
5.0%
65 175
 
4.8%
90 147
 
4.0%
68 121
 
3.3%
67 86
 
2.4%
Other values (62) 1433
39.2%
ValueCountFrequency (%)
44 1
 
< 0.1%
45 2
 
0.1%
46 1
 
< 0.1%
47 1
 
< 0.1%
48 3
 
0.1%
50 21
0.6%
52 16
0.4%
53 10
 
0.3%
54 11
 
0.3%
55 32
0.9%
ValueCountFrequency (%)
143 1
 
< 0.1%
140 1
 
< 0.1%
130 1
 
< 0.1%
125 3
 
0.1%
122 2
 
0.1%
120 6
 
0.2%
115 5
 
0.1%
112 2
 
0.1%
110 30
0.8%
108 5
 
0.1%

glucose
Real number (ℝ)

HIGH CORRELATION 

Distinct138
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.856127
Minimum40
Maximum394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2024-11-10T23:06:29.911632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile62
Q171
median78
Q387
95-th percentile108
Maximum394
Range354
Interquartile range (IQR)16

Descriptive statistics

Standard deviation23.910128
Coefficient of variation (CV)0.29209943
Kurtosis60.097287
Mean81.856127
Median Absolute Deviation (MAD)8
Skewness6.2802651
Sum299266
Variance571.69421
MonotonicityNot monotonic
2024-11-10T23:06:29.960745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 180
 
4.9%
77 166
 
4.5%
70 150
 
4.1%
73 146
 
4.0%
83 145
 
4.0%
78 139
 
3.8%
80 136
 
3.7%
74 136
 
3.7%
76 121
 
3.3%
85 119
 
3.3%
Other values (128) 2218
60.7%
ValueCountFrequency (%)
40 2
 
0.1%
43 1
 
< 0.1%
44 2
 
0.1%
45 4
 
0.1%
47 3
 
0.1%
50 3
 
0.1%
52 2
 
0.1%
53 5
 
0.1%
54 5
 
0.1%
55 13
0.4%
ValueCountFrequency (%)
394 2
0.1%
386 1
< 0.1%
370 1
< 0.1%
368 1
< 0.1%
348 1
< 0.1%
332 1
< 0.1%
325 1
< 0.1%
320 1
< 0.1%
294 1
< 0.1%
292 1
< 0.1%

TenYearCHD
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
0
3099 
1
557 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3099
84.8%
1 557
 
15.2%

Length

2024-11-10T23:06:30.003943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-10T23:06:30.039317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3099
84.8%
1 557
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 3099
84.8%
1 557
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3099
84.8%
1 557
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3099
84.8%
1 557
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3099
84.8%
1 557
 
15.2%

Interactions

2024-11-10T23:06:28.165157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.198615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.518753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.781909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.139582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.395430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.644076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.897196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:28.202582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.269804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.555669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.920361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.171139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.426978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.675093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.933379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:28.250772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.307510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.588342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.952547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.203206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.459249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.706595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.967819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:28.291821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.343594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.620437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.982980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.233300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.489952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.736588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:28.000361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:28.326822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.378055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.651420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.013250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.272913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.521674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.775997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:28.035633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:28.365476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.413760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.682842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.045669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.302614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.551048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.805867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:28.067306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:28.402299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.447677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.712754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.075836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.332600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.581144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.834238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:28.097920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:28.439194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.482658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:26.744755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.106685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.363372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.611798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:27.864950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-10T23:06:28.130025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-10T23:06:30.072093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
BMIBPMedsTenYearCHDagecigsPerDaycurrentSmokerdiaBPdiabeteseducationglucoseheartRatemaleprevalentHypprevalentStrokesysBPtotChol
BMI1.0000.1440.0890.147-0.1320.1460.3780.1050.0830.0690.0610.2070.2930.2200.3260.150
BPMeds0.1441.0000.0850.1440.0230.0480.2240.0410.0000.0880.0770.0480.2610.1010.3060.090
TenYearCHD0.0890.0851.0000.2290.0520.0080.1680.0900.0880.1230.0150.0890.1800.0400.2150.096
age0.1470.1440.2291.000-0.2110.2210.2120.1040.1400.114-0.0050.0190.3000.0620.3860.291
cigsPerDay-0.1320.0230.052-0.2111.0000.848-0.0930.0000.043-0.0860.0660.3380.1080.000-0.116-0.042
currentSmoker0.1460.0480.0080.2210.8481.0000.1160.0370.0590.0780.0700.2060.1060.0300.1260.041
diaBP0.3780.2240.1680.212-0.0930.1161.0000.0520.0440.0520.1790.0660.6420.0480.7800.195
diabetes0.1050.0410.0900.1040.0000.0370.0521.0000.0410.7170.0600.0000.0770.0000.1160.100
education0.0830.0000.0880.1400.0430.0590.0440.0411.0000.0280.0460.1360.0850.0140.0700.020
glucose0.0690.0880.1230.114-0.0860.0780.0520.7170.0281.0000.0990.0000.0870.0300.1200.033
heartRate0.0610.0770.015-0.0050.0660.0700.1790.0600.0460.0991.0000.1100.1420.0000.1750.094
male0.2070.0480.0890.0190.3380.2060.0660.0000.1360.0000.1101.0000.0000.0000.1030.081
prevalentHyp0.2930.2610.1800.3000.1080.1060.6420.0770.0850.0870.1420.0001.0000.0600.7160.160
prevalentStroke0.2200.1010.0400.0620.0000.0300.0480.0000.0140.0300.0000.0000.0601.0000.0660.000
sysBP0.3260.3060.2150.386-0.1160.1260.7800.1160.0700.1200.1750.1030.7160.0661.0000.234
totChol0.1500.0900.0960.291-0.0420.0410.1950.1000.0200.0330.0940.0810.1600.0000.2341.000

Missing values

2024-11-10T23:06:28.493805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-10T23:06:28.551215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

maleageeducationcurrentSmokercigsPerDayBPMedsprevalentStrokeprevalentHypdiabetestotCholsysBPdiaBPBMIheartRateglucoseTenYearCHD
01394.000.00.0000195.0106.070.026.9780.077.00
10462.000.00.0000250.0121.081.028.7395.076.00
21481.0120.00.0000245.0127.580.025.3475.070.00
30613.0130.00.0010225.0150.095.028.5865.0103.01
40463.0123.00.0000285.0130.084.023.1085.085.00
50432.000.00.0010228.0180.0110.030.3077.099.00
60631.000.00.0000205.0138.071.033.1160.085.01
70452.0120.00.0000313.0100.071.021.6879.078.00
81521.000.00.0010260.0141.589.026.3676.079.00
91431.0130.00.0010225.0162.0107.023.6193.088.00
maleageeducationcurrentSmokercigsPerDayBPMedsprevalentStrokeprevalentHypdiabetestotCholsysBPdiaBPBMIheartRateglucoseTenYearCHD
42241472.013.00.0000198.0120.080.025.2375.076.00
42251454.0143.00.0000216.0137.585.024.2483.0105.00
42261581.000.00.0000233.0125.584.026.0567.076.01
42271434.0120.00.0000187.0129.588.025.6280.075.00
42280501.000.00.0011260.0190.0130.043.6785.0260.00
42311583.000.00.0010187.0141.081.024.9680.081.00
42321681.000.00.0010176.0168.097.023.1460.079.01
42331501.011.00.0010313.0179.092.025.9766.086.01
42341513.0143.00.0000207.0126.580.019.7165.068.00
42370522.000.00.0000269.0133.583.021.4780.0107.00